上海燃料油期货市场有效性的计量实证研究

已有文献对我国期货市场有效性的实证检验主要针对铜、铝等金属期货,而我国燃料油期货市场是一个快速发展的新兴市场,在国际市场上的影响力正逐步增大。因此运用ADF单位根检验、JJ协整检验等时间序列计量分析技术,对上海期交所燃料油期货市场的有效性进行实证研究有助于我们提出进一步发展的政策建议。结果表明:沪燃料油期货市场已达到弱式有效,当时间跨度在两个月以内时,燃料油期货价格与现货价格存在协整关系。     

我国现阶段股指期货市场效率性实证检验--基于误差修正模型检验

沪深300股指期货作为现阶段我国两大金融期货品种中成交量、成交额最大的品种,其市场表现对金融期货市场推出新品种有指导作用。基于此,本文将检验股指期货市场效率性作为判断该市场成熟度的一个手段,首先提出对股指期货当月连续期收盘价以成交量为权重进行加权,接着通过建立误差修正模型说明期货市场规避风险、价格发现的功能已基本达到,从而说明我国股指期货市场经过4年运行已达到弱势有效。     

燃料油期货价格波动的杠杆效应

近年来国际石油价格的大幅而频繁的波动引发了各国对于能源安全和能源危机的高度关注,建立战略石油储备和能源金融衍生品市场是解决这一问题的重要措施。本文利用E-     

离岸与在岸股指期货市场的价格联动关系研究——基于国内股指期货市场受限的实证分析

金融资产的价格发现权是各国经济主权的重要组成部分,关系到市场秩序和国民财富的安全。从历史经验看,一旦在岸市场出现发展迟滞或过度管制等问题,竞争性离岸市场就会利用契机快速发展。以2015年国内股指期货受限事件为自然实验,分析新加坡交易所A50与国内沪深300股指期货的价格联动关系,研究表明：国内市场受限后,A50股指期货的持仓量呈明显上升趋势,承载的避险需求增大,其夜盘和盘前涨跌能有效预测沪深300指数开盘走势;在同步交易时段,沪深300股指期货在价格发现中的贡献度为64.4%,仍明显高于A50股指期货;境内熔断触发后,A50股指期货的成交量没有显著减少,表明在岸市场暂停无法显著制约离岸市场的价格发现能力。     

收入不平等和经济增长非线性关系——中国数据的实证检验

收入不平等和经济增长的关系一直是经济学研究中的热点问题之一,尽管研究成果汗牛充栋,但仍存在诸多争论。本文在对文献综合分析的基础上,采用平滑转移模型来研究中国收入不平等和经济增长的非线性关系,并分别检验了可能是两者关系转移变量的诸多因素。研究结果表明中国收入不平等和经济增长的关系在人均收入2115元(以1978年不变价为基期)处发生转移,在此之前收入不平等对经济增长具有一定的促进作用,但在此之后,收入不平等对经济增长产生严重的损害作用。结合现状,本文也简要分析了导致该转变的原因。     

培育和完善北京市房屋租赁市场政策建议

房屋租赁市场是房地产市场的重要组成部分,无论是在拉动投资,还是在启动消费方面都起着房屋买卖市场所不能替代的作用。北京市的房屋租赁市场的发育落后于沪、深等城市.特别是行政管理体制远远不能满足市场需要.尚属新兴的隐形市场,但发展前景乐观,政府在培育和发展房屋租赁市场方面大有可为。     

完善我国房地产市场调控长效机制的政策建议

进入2012年第四季度,我国大部分城市房地产价格,尤其是北京、上海等一线城市,出现了新一轮较大幅度的上涨。受政策预期影响,今年房地产市场整体交易活跃,延续了去年四季度以来量价齐升的态势。同时,各地市场分化严重,     

坚持房地产调控不动摇

<正>最近以来,我国房地产市场调控成效已经出现积极的变化。据国家统计局11月份发布的70个大中城市住宅销售价格统计数据显示,10月份,我国70个大中城市房价平均环比下降0.14%。新建商品住宅方面,环比价格下降的城市有34个,比9月份增加了17个。价格下降的城市与去年10月相比有2个,与今年9月份相比,增加了1个。二手房方面,环比价格下降的城市有38个,     

现阶段完善中国温室气体减排政策的思路和建议

中国政府近年来采用了一系列节能减排、减缓气候变化的行动和政策,温室气体减排工作取得了显著的成效。但中国的减排政策体系尚不完善,导致了"拉闸限电"等突出问题。因此有必要对现阶段中国温室气体减排政策作进一步探讨。     

Forecasting crude oil futures market returns: A principal component analysis combination approach

To improve the predictability of crude oil futures market returns, this paper proposes a new combination approach based on principal component analysis (PCA). The PCA combination approach combines individual forecasts given by all PCA subset regression models that use all potential predictor subsets to construct PCA indexes. The proposed method can not only guard against over-fitting by employing the PCA technique but also reduce forecast variance due to extensive forecast combinations, thus benefiting from both the combination of information and the combination of forecasts. Showing impressive out-of-sample forecasting performance, the PCA combination approach outperforms a benchmark model and many related competing models. Furthermore, a mean–variance investor can realize sizeable utility gains by using the PCA combination forecasts relative to the competing forecasts from an asset allocation perspective.     

The role of oil futures intraday information on predicting US stock market volatility

This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework. In-sample results indicate that oil futures intraday information is helpful to increase the predictability. Moreover, compared to the benchmark model, the proposed models improve their predictive ability with the help of oil futures realized volatility. In particular, the multivariate HAR model outperforms the univariate model. Accordingly, considering the contemporaneous connection is useful to predict the US stock market volatility. Furthermore, these findings are consistent across a variety of robust checks.     

New perspectives on forecasting inflation in emerging market economies: An empirical assessment

We use a broad-range set of inflation models and pseudo out-of-sample forecasts to assess their predictive ability among 14 emerging market economies (EMEs) at different horizons (1–12 quarters ahead) with quarterly data over the period 1980Q1-2016Q4. We find, in general, that a simple arithmetic average of the current and three previous observations (the RW-AO model) consistently outperforms its standard competitors—based on the root mean squared prediction error (RMSPE) and on the accuracy in predicting the direction of change. These include conventional models based on domestic factors, existing open-economy Phillips curve-based specifications, factor-augmented models, and time-varying parameter models. Often, the RMSPE and directional accuracy gains of the RW-AO model are shown to be statistically significant. Our results are robust to forecast combinations, intercept corrections, alternative transformations of the target variable, different lag structures, and additional tests of (conditional) predictability. We argue that the RW-AO model is successful among EMEs because it is a straightforward method to downweight later data, which is a useful strategy when there are unknown structural breaks and model misspecification.     

上市公司产品市场竞争与资本结构选择的实证分析

本文通过对2001￣2003年我国沪深两地上市公司的实证研究表明,产品市场竞争程度是资本结构决定的重要影响因素,产品市场竞争程度与资本结构的选择高度正相关,认为这种经营风险与财务风险不匹配现象与我国上市公司的股权融资效率低下和上市公司的竞争性战略选择有关。     

A study of the efficiency of the Chinese clean energy stock market and its correlation with the crude oil market based on an asymmetric multifractal scaling behavior analysis

We examine the multifractal scaling behavior and market efficiency of China’s clean energy stock indexes using an asymmetric multifractal detrended fluctuation analysis (A-MFDFA) and then investigate the tail correlation between this index and the crude oil market via an asymmetric multifractal detrended cross-correlation analysis (A-MFDCCA). First, we reveal that the overall, upward and downward trends of the clean energy stock indexes all have significant multifractal characteristics. The clean energy stock market is far from efficient regardless of whether the fluctuations are small or large. In addition, both upward and downward fluctuations exhibit considerable asymmetry. The significant gap between the downward and overall trends indicates that the downward trend following small-scale fluctuations implies weaker efficiency for investors. Furthermore,based on the sliding market deficiency measure (MDM),we find that the change in efficiency in the three trends significantly depends on the length of the window. In the short term, there is no significant efficiency difference among these three trends; however, in the long term, the asymmetry in the upward and downward trends has gradually increased,especially after December 2018. The results demonstrate that bear markets can offer considerably more opportunities for obtaining excess profits. Finally, we reveal that the cross-correlation between the trends of crude oil prices and low-carbon indexes exhibits significant multifractal characteristics. When the crude oil market is in a bull market or the low-carbon energy market is in a bear market, especially in a larger-scale fluctuation, investors should pay attention to the long-term influence of the counterparty market and carry out a hedging operation to avoid risks.     

Forecasting crude oil market volatility using variable selection and common factor

This paper aims to improve the predictability of aggregate oil market volatility with a substantially large macroeconomic database, including 127 macro variables. To this end, we use machine learning from both the variable selection (VS) and common factor (i.e., dimension reduction) perspectives. We first use the lasso, elastic net (ENet), and two conventional supervised learning approaches based on the significance level of predictors’ regression coefficients and the incremental R-square to select useful predictors relevant to forecasting oil market volatility. We then rely on the principal component analysis (PCA) to extract a common factor from the selected predictors. Finally, we augment the autoregression (AR) benchmark model by including the supervised PCA common index. Our empirical results show that the supervised PCA regression model can successfully predict oil market volatility both in-sample and out-of-sample. Also, the recommended models can yield forecasting gains in both statistical and economic perspectives. We further shed light on the nature of VS over time. In particular, option-implied volatility is always the most powerful predictor.     

专业系统大宗交易事件对二级市场影响的实证研究

文章试图通过实证研究专业系统大宗交易事件对二级市场的影响,分析了大宗交易系统在限售股解禁后减持中的重要作用,并提出了有利于市场更健康发展的一些政策建议。     

Dynamic relations between oil and stock market returns: A multi-country study

We study the relation between the BRENT and seventeen stock market indexes of important oil-dependent economies. We focus on connectedness between these markets and characterize the dynamics of transmission and reception. We use LASSO methods to shrink, select, and estimate the high dimensional network linking these markets between August, 1999 and March, 2018. This methodological innovation allows the inclusion of a significantly larger number of markets in the network, providing finer results regarding connectedness in the oil-stock market nexus. We show that transmission runs mainly from stock markets to the BRENT. Connectedness varies considerably over time, reaching peaks during times of financial distress. Dynamic predictive causality tests show evidence of time-varying bidirectional causality. Causality from stock markets to the BRENT is detected mostly for the last part of the sample period. This finding indicates that the impact of stock market developments on oil markets is growing over time.     

竞争视角下市场占有均衡：理论模型与现实解释

市场占有行为是经济主体的理性行为,市场占有的实现方式有市场竞争与政策约束。文章构建市场占有均衡模型,指出经济主体基于自身的市场占有偏好进行市场占有,并根据市场收益的增减或发展目标的重新设计分别对市场占有偏好进行自发性纠正、战略性纠正,从而形成新的市场占有均衡。市场占有均衡因对手的竞争反应的差异而表现出不同的均衡状态。基于市场占有均衡模型,文章以新的角度审视了公共产品供给不足、厂商恶性竞争及国企改革等经济现象。     

China and international market integration: Evidence from the law of one price in the Middle East and Africa

We model market integration in the Middle East and Africa by analyzing price dispersion and testing the law of one price (LOP) on highly-comparable actual local retail prices of 135 goods and services across 23 countries in the region over the period of 1990–2016. Second-generation panel estimators are applied to four price benchmarks: Regional average, South Africa, China, and US prices. Cross-regional price dispersion diminishes considerably over time up to 2008, particularly for non-tradeables around China price. The test of LOP indicates the percentage of convergent prices is highest in China price benchmark, followed by US, South Africa, and regional average benchmarks. Direct estimation of the convergence speed confirms this order. Overall, the results show evidence of increasing market integration in Middle East and Africa but it appears to be driven by global forces and, especially, the rise of China as a new economic power. The results show that some emerging market economies, such as China, can step up and promote integration while traditional economic powerhouses, such as the USA and UK, disengage from international economic relations.